ArticleScientific data2026
GaitIntent: A Kinematic Dataset Focusing on Steady-State to Transitional Locomotion for Lower-Limb Motion Rehabilitation.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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8 authors.
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Abstract
Motion intent recognition (MIR), the real-time interpretation of user movement, is crucial for advanced motion rehabilitation. MIR enables responsive control of prostheses and exoskeletons, enhancing mobility for lower-limb impaired individuals. Most existing datasets prioritize steady-state locomotion, often overlooking complex transitional movements critical in daily activities, which limits anticipatory control development. We introduce GaitIntent, a novel kinematic dataset. Inspired by gait symmetry, it uses sound limb kinematics for predictive MIR for the affected side-an approach less comprehensively covered. We collected multi-node Inertial Measurement Units (IMUs) data from the thigh, shank, and foot of 11 subjects' sound limbs performing 13 daily activities, emphasizing eight distinct transitional movements. GaitIntent provides a valuable resource for developing low-latency, robust algorithms for wearable rehabilitation and human-centric control systems.
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